U.S. patent application number 12/104022 was filed with the patent office on 2009-10-22 for in-vehicle sensor-based calibration algorithm for yaw rate sensor calibration.
This patent application is currently assigned to GM GLOBAL TECHNOLOGY OPERATIONS, INC.. Invention is credited to Chaminda Basnayake.
Application Number | 20090265054 12/104022 |
Document ID | / |
Family ID | 41199646 |
Filed Date | 2009-10-22 |
United States Patent
Application |
20090265054 |
Kind Code |
A1 |
Basnayake; Chaminda |
October 22, 2009 |
IN-VEHICLE SENSOR-BASED CALIBRATION ALGORITHM FOR YAW RATE SENSOR
CALIBRATION
Abstract
A system and method for calibrating a vehicle heading sensor,
such as a yaw-rate sensor, when GPS signals are not available using
a bias update model that employs a bias gain factor. In order for
the bias update model to be accurate, the vehicle should be
traveling relatively straight. One embodiment of the present
invention uses three thresholds to determine if the vehicle is
traveling straight. These thresholds include a yaw-rate threshold,
a steering wheel angle threshold and a wheel speed threshold. If
all three of the thresholds indicate that the vehicle is traveling
straight, then the update bias model can be used to calibrate the
yaw-rate sensor.
Inventors: |
Basnayake; Chaminda;
(Windsor, CA) |
Correspondence
Address: |
MILLER IP GROUP, PLC;GENERAL MOTORS CORPORATION
42690 WOODWARD AVENUE, SUITE 200
BLOOMFIELD HILLS
MI
48304
US
|
Assignee: |
GM GLOBAL TECHNOLOGY OPERATIONS,
INC.
Detroit
MI
|
Family ID: |
41199646 |
Appl. No.: |
12/104022 |
Filed: |
April 16, 2008 |
Current U.S.
Class: |
701/31.4 |
Current CPC
Class: |
B60W 40/114 20130101;
B60W 40/11 20130101; G01C 21/28 20130101; B60W 40/112 20130101;
G01C 25/00 20130101 |
Class at
Publication: |
701/29 |
International
Class: |
G01M 17/00 20060101
G01M017/00 |
Claims
1. A yaw-rate sensor calibration system in a vehicle, said vehicle
including four wheels, said system comprising: a yaw-rate sensor
providing a yaw-rate signal indicating a yaw of the vehicle; a
hand-wheel angle sensor providing a rotation signal of the rotation
of a steering wheel of the vehicle; a plurality of wheel speed
sensors for providing wheel speed signals of the speed of the
wheels of the vehicle; and a yaw-rate sensor calibration controller
for calibrating the yaw-rate sensor using a bias update model, said
controller being responsive to the yaw-rate signal, the rotation
signal and the wheel speed signals, said calibration controller
determining whether the vehicle is traveling relatively straight
using separate calculations for each of the yaw-rate signal, the
hand-wheel angle signal and the wheel speed signals, said
controller calibrating the yaw-rate sensor if the vehicle is
traveling relatively straight.
2. The system according to claim 1 further comprising a GPS
receiver providing GPS signals to the calibration controller
indicating the position of the vehicle, said calibration controller
using the GPS signals to calibrate the yaw-rate sensor when the GPS
signals are available and using the bias update model to calibrate
the yaw-rate sensor when the GPS signals are not available.
3. The system according to claim 1 wherein the calibration
controller calibrates the yaw-rate sensor using the bias update
model by calculating a yaw bias using the following equation:
YawBias.sub.i=(1-.beta..sub.CHUPT)YawBias.sub.i-l+.beta..sub.CHUPTYawRate-
.sub.i,CHUPT where YawBias.sub.i is the yaw bias and
.beta..sub.CHUPT is a bias gain factor.
4. The system according to claim 1 wherein the calibration
controller determines whether the vehicle is traveling relatively
straight using a standard deviation of the yaw-rate signal and the
equation: std(YawRate.sub.i-N:i)<.gradient..sub.YawSTD where N
is a yaw-rate window length, YawRate is the standard deviation of
the yaw-rate signal and .gradient..sub.YawSTD is a yaw-rate
standard deviation threshold.
5. The system according to claim 1 wherein the calibration
controller determines whether the vehicle is traveling straight
using the rotation signal and the equation:
std(SteeringWheelAng.sub.i-P:i)<.gradient..sub.SteerAngSTD where
P is a steering wheel angle window length, SteeringWheelAngle is
the standard deviation of the rotation signal, and
.gradient..sub.SteerAngSTD is a steering wheel angle standard
deviation threshold.
6. The system according to claim 1 wherein the calibration
controller determines whether the vehicle is traveling straight
using the wheel speed signals and the equation:
|WheelSpeed.sub.L-WheelSpeed.sub.R|<.gradient..sub.dWheelSpeed
where .gradient..sub.dWheelSpeed is a differential wheel speed
threshold, WheelSpeed.sub.L is the speed of a non-driven left wheel
speed and WheelSpeed.sub.R is the speed of a non-driven right wheel
speed.
7. A heading sensor calibration system in a vehicle, said vehicle
including four wheels, said system comprising: a heading sensor
providing a heading signal indicating a heading of the vehicle; a
plurality of vehicle sensors providing sensor signals identifying
parameters of the vehicle; and a heading sensor calibration
controller for calibrating the heading sensor using a bias update
model, said controller being responsive to the heading signal and
the sensor signals, said controller using the heading signal and
the sensor signals to determine whether the vehicle is traveling
relatively straight, said controller calibrating the heading sensor
if the vehicle is traveling relatively straight.
8. The system according to claim 7 wherein the heading sensor is a
yaw-rate sensor providing a yaw-rate signal indicating a yaw of the
vehicle.
9. The system according to claim 8 wherein the calibration
controller calibrates the yaw-rate sensor using the bias update
model by calculating a yaw bias using the following equation:
YawBias.sub.i=(1-.beta..sub.CHUPT)YawBias.sub.i-l+.beta..sub.CHUPTYawRate-
.sub.i,CHUPT where YawBias.sub.i is the yaw bias and
.beta..sub.CHUPT is a bias gain factor.
10. The system according to claim 7 wherein the plurality of
vehicle sensors include a hand-wheel angle sensor providing a
rotation signal of the rotation of a steering wheel of the vehicle
and a plurality of wheel speed sensors for providing wheel speed
signals of the speed of the wheel of the vehicle, said calibration
controller using the rotation signal, the heading signal and the
wheel speed signals to determine whether the vehicle is traveling
relatively straight.
11. The system according to claim 10 wherein the calibration
controller determines whether the vehicle is traveling relatively
straight using the heading signal and the equation:
std(YawRate.sub.i-N:i)<.gradient..sub.YawSTD where N is a window
length, YawRate is the standard deviation of the yaw-rate signal
and .gradient..sub.YawSTD is a yaw-rate standard deviation
threshold.
12. The system according to claim 10 wherein the calibration
controller determines whether the vehicle is traveling straight
using the rotation signal and the equation:
std(SteeringWheelAng.sub.i-P:i)<.gradient..sub.SteerAngSTD where
P is a steering wheel angle window length, SteeringWheelAngle is
the standard deviation of the rotation signal, and
.gradient..sub.SteerAngSTD is a steering wheel angle standard
deviation threshold.
13. The system according to claim 10 wherein the calibration
controller determines whether the vehicle is traveling straight
using the wheel speed signals and the equation:
|WheelSpeed.sub.L-WheelSpeed.sub.R|<.gradient..sub.dWheelSpeed
where .gradient..sub.dWheelSpeed is a differential wheel speed
threshold, WheelSpeed.sub.L is the speed of a non-driven left wheel
speed and WheelSpeed.sub.R is the speed of a non-driven right wheel
speed.
14. The system according to claim 7 further comprising a GPS
receiver providing GPS signals to the calibration controller
indicating the position of the vehicle, said calibration controller
using the GPS signals to calibrate the heading sensor when the GPS
signals are available and using the bias update model to calibrate
the heading sensor when the GPS signals are not available.
15. A yaw-rate sensor calibration system in a vehicle, said vehicle
including four wheels, said system comprising: a yaw-rate sensor
providing a yaw-rate single indicating a yaw of the vehicle; a
hand-wheel angle sensor providing a rotation signal of the rotation
of a steering of the vehicle; a plurality of wheel speed sensors
for providing wheel speed signals of the speed of the wheels of the
vehicle; a GPS receiver providing GPS signals indicating the
position of the vehicle; and a yaw-rate sensor calibration
controller for calibrating the yaw-rate sensor using a bias update
model, said controller being responsive to the yaw-rate signal, the
hand-wheel rotation signal, the wheel speed signals and the GPS
signals, said calibration controller using the GPS signals to
calibrate the yaw-rate sensor when the GPS signals are available
and using the bias update model to calibrate the yaw-rate sensor
when the GPS signals are not available and the vehicle is traveling
relatively straight, said calibration controller determining
whether the vehicle is traveling relatively straight using separate
calculations for each of the yaw-rate signal, the hand-wheel angle
signal and the wheel speed signals.
16. The system according to claim 15 wherein the calibration
controller calibrates the yaw-rate sensor using the bias update
model by calculating a yaw bias using the following equation:
YawBias.sub.i=(1-.beta..sub.CHUPT)YawBias.sub.i-l+.beta..sub.CHUPTYawRate-
.sub.i,CHUPT where YawBias.sub.i is the yaw bias and
.beta..sub.CHUPT is a bias gain factor.
17. The system according to claim 15 wherein the calibration
controller determines whether the vehicle is traveling relatively
straight using the yaw-rate signal and the equation:
std(YawRate.sub.i-N:i)<.gradient..sub.YawSTD where N is a
yaw-rate window length, YawRate is the standard deviation of the
yaw-rate signal and .gradient..sub.YawSTD is a yaw-rate standard
deviation threshold.
18. The system according to claim 15 wherein the calibration
controller determines whether the vehicle is traveling straight
using the rotation signal and the equation:
std(SteeringWheelAng.sub.i-P:i)<.gradient..sub.SteerAngSTD where
P is a steering wheel angle window length, SteeringWheelAngle is
the standard deviation of the rotation signal, and
.gradient..sub.SteerAngSTD is a steering wheel angle standard
deviation threshold.
19. The system according to claim 15 wherein the calibration
controller determines whether the vehicle is traveling straight
using the wheel speed signals and the equation:
|WheelSpeed.sub.L-WheelSpeed.sub.R|<.gradient..sub.dWheelSpeed
where .gradient..sub.dWheelSpeed is a differential wheel speed
threshold, WheelSpeed.sub.L is the speed of a non-driven left wheel
speed and WheelSpeed.sub.R is the speed of a non-driven right wheel
speed.
Description
BACKGROUND OF THE INVENTION
[0001] 1. Field of the Invention
[0002] This invention relates generally to a system and method for
calibrating a heading sensor, such as a yaw-rate sensor, and, more
particularly, to a system and method for removing sensor bias
errors from a yaw-rate sensor to use the yaw-rate sensor to provide
an accurate vehicle heading when GPS signals are not available,
where the system and method employ a bias update model to calibrate
the sensor using the yaw-rate, a steering wheel angle and a
differential wheel speed to identify time windows where the vehicle
is traveling relatively straight.
[0003] 2. Discussion of the Related Art
[0004] GPS signals, or other Global Navigation Satellite System
(GNSS) signals, can provide accurate positioning and navigation.
However, GPS receivers suffer from sky visibility-related
limitations, for example, in urban canyons and areas with dense
tree cover. Further, GPS signals may suffer from multi-path errors
or cross-correlation errors in such areas. Because of existing
highly sensitive and fast reacquisition GPS technology, accurate
GPS signals become available when sky visibility is temporarily
improved for short durations, such as 10-20 seconds, even in less
than optimum environments. Therefore, the continuity of GPS
technology comes down to maintaining positioning accuracy through
GPS outages between GPS available time windows.
[0005] Automotive-grade inertial sensors, such as yaw-rate sensors
and accelerometers, have highly variable bias and scale
characteristics that cause sensor drift that typically makes them
un-suitable for navigation and heading determination functions
without proper error correction techniques. For example, certain
automotive-grade yaw-rate sensors allow up to 2 deg/sec variations
for the yaw-rate sensor bias. If such a variability is not
corrected, and is allowed for over a period of two minutes, a
yaw-rate sensor starting with a bias of 0 deg/sec at zero seconds
could reach a bias of 2 deg/sec after 120 seconds. If a linear
growth of bias were assumed for simplicity, a heading change
derived by integrating yaw-rate sensor signals that is not
calibrated would indicate a heading change of 120.degree. only as a
result of the variation of the bias.
[0006] Inertial sensors can be used in combination with GPS
receivers to provide a reasonably accurate vehicle heading, and
position if a distance measure, such as vehicle wheel speeds, are
available, even when the GPS signals are not available. However,
automotive-grade inertial sensors do not typically provide the same
level of accuracy as GPS signals. GPS/inertial sensor integrated
systems can calibrate the inertial sensors and maintain vehicle
heading and position accuracy using GPS signals when the GPS
signals are available, and use the calibrated inertial sensors when
the GPS signals are not available to maintain a heading and a
position solution until the GPS signals become available again.
[0007] Known yaw-rate sensor calibration algorithms typically
approach bias and scale calibration as a two-step process, and
require specific vehicle maneuvers to be performed for the
calibration. For example, sensor bias calibration may require the
vehicle to be driven in a straight line or be stationary for a
known period of time so that the accumulated heading error can be
directly estimated as a result of sensor bias error. For scale
calibration, the vehicle may be required to be driven through a
controlled turn.
[0008] U.S. patent application Ser. No. 11/770,898, title GPS-Based
In-Vehicle Sensor Calibration Algorithm, filed Jun. 29, 2007,
assigned to the assignee of this application and herein
incorporated by reference, discloses a system and method for
calibrating a heading sensor using GPS signals. The system receives
wheel speed or rotation signals, a vehicle odometer reading, GPS
signals and yaw-rate signals, and uses the GPS signals to calibrate
the heading sensor while the GPS signals are available.
[0009] As discussed above, the '898 application calibrates the
heading sensor using GPS signals when they are available, so that
when the GPS signals are not available the heading sensor will be
fairly accurate for some period of time. However, if the GPS
signals are unavailable for an extended period of time, then it may
be desirable to calibrate the heading sensor when the GPS signals
are not available to maintain the accuracy of the heading
sensor.
SUMMARY OF THE INVENTION
[0010] In accordance with the teachings of the present invention, a
system and method are disclosed for calibrating a vehicle heading
sensor, such as a yaw-rate sensor, when GPS signals are not
available using a bias update model that employs a bias gain
factor. In order for the bias update model to be accurate, the
vehicle should be traveling relatively straight. One embodiment of
the present invention uses three thresholds to determine if the
vehicle is traveling straight. These thresholds include a yaw-rate
threshold, a steering wheel angle threshold and a wheel speed
threshold. If all three of these thresholds indicate that the
vehicle is traveling straight, then the update bias model can be
used to calibrate the yaw-rate sensor.
[0011] Additional features of the present invention will become
apparent from the following description and appended claims taken
in conjunction with the accompanying drawings.
BRIEF DESCRIPTION OF THE DRAWINGS
[0012] FIG. 1 is a plan view of a vehicle including a system for
providing yaw-rate sensor calibration, according to an embodiment
of the present invention; and
[0013] FIG. 2 is a flow chart diagram showing a process for
calibrating a yaw-rate sensor using a bias update model, according
to an embodiment of the present invention.
DETAILED DESCRIPTION OF THE EMBODIMENTS
[0014] The following discussion of the embodiments of the invention
directed to a system and method for calibrating a yaw-rate sensor
when GPS signals are not available using a bias update model is
merely exemplary in nature, and is in no way intended to limit the
invention or its applications or uses.
[0015] FIG. 1 is a plan view of a vehicle 10 including a yaw-rate
sensor calibration controller 12, according to an embodiment of the
present invention. The vehicle 10 also includes front wheels 14 and
16 and rear wheels 18 and 20. The wheels 14, 16, 18 and 20 each
include a wheel speed sensor 22, 24, 26 and 28, respectively, that
provide wheel speed and/or wheel rotation signals to the controller
12. A GPS receiver 32 provides GPS signals to the controller 12,
and a yaw-rate sensor 34 provides vehicle yaw rate sensor signals
to the controller 12. Also, a hand-wheel angle sensor 36 provides a
steering wheel angle signal of the rotation of a steering wheel 38
to the controller 12.
[0016] The present invention proposes using a constant heading
update (CHUPT) algorithm that employs a bias update model in the
controller 12 for calibrating the yaw-rate sensor 34 when the GPS
signals are not available. Although, the bias update model
calibrates the yaw-rate sensor 34, in other embodiments, any
suitable heading or inertial sensor that provides vehicle heading
can be calibrated by the CHUPT algorithm. The CHUPT algorithm
calculates a yaw bias signal YawBias.sub.i that is used to reduce
the bias error of the yaw-rate sensor 34 so that it provides an
accurate heading reading.
[0017] In this embodiment, the bias update model is defined as:
YawBias.sub.i=(1-.beta..sub.CHUPT)YawBias.sub.i-l+.beta..sub.CHUPTYawRat-
e.sub.i,CHUPT (1)
Where .beta..sub.CHUPT is a bias gain factor.
[0018] In order for the bias update model to be accurate, the
vehicle 10 needs to be traveling relatively straight. The CHUPT
algorithm uses vehicle yaw-rate, steering wheel angle and
differential wheel speeds to identify time windows where a vehicle
heading is relatively constant, i.e., the vehicle is traveling
straight. The extent of how straight the vehicle travel needs to be
and how long the time window can be are controlled by four
predetermined parameters, namely, a yaw standard deviation
threshold .gradient..sub.YawSTD, a steering wheel angle standard
deviation threshold .gradient..sub.SteerAngSTD, a differential
wheel speed threshold .gradient..sub.dWheelSpeed and time window
lengths.
[0019] Equations (2) and (3) below identify how the algorithm
determines if the standard deviation of the yaw-rate signal YawRate
is less than the yaw standard deviation threshold
.gradient..sub.YawSTD and if the standard deviation of the steering
wheel angle signal SteeringWheelAng during the time window P is
less than the steering wheel angle standard deviation threshold
.gradient..sub.SteerAngSTD, respectively.
std(YawRate.sub.i-N:i)<.gradient..sub.YawSTD (2)
Where N is a yaw-rate window length.
std(SteeringWheelAng.sub.i-P:i)<.gradient..sub.SteerAngSTD
(3)
Where P is a steering wheel angle window.
[0020] The conditions of equations (2) and (3) can still be
fulfilled in scenarios where the vehicle 10 is traveling along a
curve and the steering wheel angle is kept constant. The yaw-rate
signal may also indicate a constant vehicle heading under these
circumstances. In such a scenario, the yaw-rate signal indicates an
actual heading rate that should not be considered as a change in
the bias. In order to avoid such misidentifications, a differential
wheel speed verification can be performed. This verification, shown
by equation (4) below, verifies that the difference between the
left and right non-driven wheel counts or speeds are only
indicating the measurement noise and no significant differences are
observed during a given time window.
|WheelSpeed.sub.L-WheelSpeed.sub.R|<.gradient..sub.dWheelSpeed
(4)
Where WheelSpeed.sub.L is the wheel speed of a left non-driven
wheel and WheelSpeed.sub.R is the wheel speed of a right non-driven
wheel.
[0021] If the steering wheel angle standard deviation and yaw-rate
standard deviation do not change beyond a predetermined thresholds
and the relative speed between the non-driven wheels also is about
the same within a predetermined threshold, then it is assumed that
the vehicle 10 is not turning. The CHUPT algorithm updates the
current yaw-rate bias YawBias.sub.i using the yaw-rate signal and
equation (1) when the conditions given in equations (2)-(4) are
met.
[0022] FIG. 2 is a flow chart diagram 40 showing the steps of the
present invention for correcting the yaw bias of the yaw-rate
sensor 34, according to an embodiment of the present invention. At
box 42, the algorithm determines whether the vehicle 10 is
traveling straight using the yaw-rate threshold calculation of
equation (2). At box 44, the algorithm determines whether the
vehicle 10 is traveling straight using the steering wheel angle
threshold calculation of equation (3). At box 46, the algorithm
determines whether the vehicle 10 is traveling straight using the
wheel speed threshold calculation of equation (4). If all of these
calculations determine that the vehicle 10 is traveling relatively
straight, then the algorithm updates or calibrates the yaw-rate
sensor 34 using the update bias model of equation (1).
[0023] The foregoing discussion discloses and describes merely
exemplary embodiments of the present invention. One skilled in the
art will readily recognize from such discussion and from the
accompanying drawings and claims that various changes,
modifications and variations can be made therein without departing
from the spirit and scope of the invention as defined in the
following claims.
* * * * *